What is the Sentiment Analysis? Use Case, Challenges, Benefits, and More.
We can surely tell that with the development of e-commerce, SaaS tools, and digital technologies, sentiment analysis is growing the thing. Let’s see what sentiment analysis is.
After a fast look into Google Trends, we can see that sentiment analysis has grown more and more popular over the years.
In this blog post, I’ll explain:
What is the Sentiment Analysis? Why Sentiment Analysis is Important?
How Sentiment Analysis is Done?
Challenges of Sentiment Analysis
Use Case of Sentiment Analysis.
Benefits of Sentiment Analysis.
What is the Sentiment Analysis?
It is a process for calculating the opinions of individuals or groups. Such as a segment of a brand’s audience or an individual customer in communication with a customer support agent. Based on a scoring device, It monitors conversations and evaluates language and voice inflections to quantify attitudes, opinions, and emotions related to a business, product or service, or topic. It is also known as opinion mining. As part of the overall speech analytics system, It is an integral part that defines a customer’s opinions or emotions.
Why Sentiment Analysis is Important?
First of all, It saves time and effort because the process of sentiment extraction is fully automatic. It’s the algorithm that analyses sentiment data, and so human participation is rare.
Secondly, It is important because emotions and attitudes towards a topic can become actionable pieces of information useful in various areas of business and research.
Due to language complexity, It has to face at least a couple of problems.
One difficulty a tool has to face is contrastive combinations. They happen when one piece of writing (a sentence) consists of two different words (both positive and negative).
Sample sentence: “The weather was terrible, but the hike was amazing!”
Extra big problem opinion Mining algorithms face named-entity identification. Words in context have various meanings.
Sample Sentence: Does “Everest” refer to the mountain or to the movie?
Also known as pronoun resolve, explains the problem of
references within a sentence: what a pronoun or a noun refers to.
Sample sentence: “We went to the theater and went for dinner. It was awful.”
Is there any opinion mining tool identifying sarcasm? Please advise one!
Sample sentence: “I’m so happy the plane is delayed.”
It just so happens that any language used online takes its personal form. The economy of language and the Internet as a common result in poor spelling, contractions, acronyms, lack of capital, and poor grammar. Analyzing such parts of writing may cause difficulties for opinion Mining algorithms.
Use Case of Sentiment Analysis
1. Segment User Groups Based on Opinions:
Tracking sentiment provides an organization to see which customers are more opinionated than others. For example, many believe that 80% of customer issues come from 20% of users. If this stat happens to be true, you will be capable of segmenting the qualities of that group, and each fixes common issues or even avoid those buyers. (Of course, avoiding users would have to mean there is little to no ROI based on the level/type of opinions of said group.)
2. Plan Product/Service Changes:
Analyzing customer opinions is a treasure trove of data, particularly when it comes to what you sell. Updating software products, increasing the design of physical goods, or bettering your services can all come from customer sentiment. At times, this data can even yield new products/services for your business to offer.
3. Plan Process Changes:
Customer sentiment isn’t always positive. But, negative feedback isn’t necessarily false. These opinions may require sorting out in a systematic way, meaning improving your overall customer service (or other) process.
4. Continuously Track Sentiment Over Time:
The sentiment is a metric worth constantly checking. As you improve both your processes and products, opinions will change. Seeing these changes allow for better navigating the turbulent waters of sentiment.
Benefits of Sentiment Analysis
A related opinion mining score provides insight into the effectiveness of call center agents and client support representatives and also serves as a useful measurement to gauge the overall opinion on a business’s products or services. When sentiment analysis scores are linked across certain segments, businesses can easily classify common pain points, areas for improvement in the delivery of customer support, and overall satisfaction between product lines or services.
By monitoring emotions and opinions about products,
services, or even customer support effectiveness continuously, brands are
capable of identifying subtle shifts in opinions and adapting readily to meet
the changing needs of their audience.
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